Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

Xinhua Gao, Song Liu (), Shan Jiang, Dennis Yu, Yong Peng, Xianting Ma and Wenting Lin
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Xinhua Gao: Chongqing Key Laboratory of Intelligent Integrated and Multidimensional Transportation System, Chongqing Jiaotong University, Chongqing 400074, China
Song Liu: Chongqing Key Laboratory of Intelligent Integrated and Multidimensional Transportation System, Chongqing Jiaotong University, Chongqing 400074, China
Shan Jiang: Chongqing City Transportation Development & Investment Group Co., Ltd., Chongqing 400074, China
Dennis Yu: The David D. Reh School of Business, Clarkson University, Potsdam, NY 13699, USA
Yong Peng: School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Xianting Ma: School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Wenting Lin: School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China

Mathematics, 2024, vol. 12, issue 7, 1-27

Abstract: To optimize the evacuation process of rail transit passenger flows, the influence of the feeder bus network on bus demand is pivotal. This study first examines the transportation mode preferences of rail transit station passengers and addresses the feeder bus network’s optimization challenge within a three-dimensional framework, incorporating an elastic mechanism. Consequently, a strategic planning model is developed. Subsequently, a multi-objective optimization model is constructed to simultaneously increase passenger numbers and decrease both travel time costs and bus operational expenses. Due to the NP-hard nature of this optimization problem, we introduce an enhanced non-dominated sorting genetic algorithm, INSGA-II. This algorithm integrates innovative encoding and decoding rules, adaptive parameter adjustment strategies, and a combination of crowding distance and distribution entropy mechanisms alongside an external elite archive strategy to enhance population convergence and local search capabilities. The efficacy of the proposed model and algorithm is corroborated through simulations employing standard test functions and instances. The results demonstrate that the INSGA-II algorithm closely approximates the true Pareto front, attaining Pareto optimal solutions that are uniformly distributed. Additionally, an increase in the fleet size correlates with greater passenger volumes and higher operational costs, yet it substantially lowers the average travel cost per customer. An optimal fleet size of 11 vehicles is identified. Moreover, expanding feeder bus routes enhances passenger counts by 18.03%, raises operational costs by 32.33%, and cuts passenger travel time expenses by 21.23%. These findings necessitate revisions to the bus timetable. Therefore, for a bus network with elastic demand, it is essential to holistically optimize the actual passenger flow demand, fleet size, bus schedules, and departure frequencies.

Keywords: feeder bus; 3D path optimization; departure schedule; multi-objective optimization; Non-dominated Sorting Genetic Algorithm II (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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